Construct a safe information visualization utility utilizing the Amazon Redshift Knowledge API with AWS IAM Identification Heart

In right this moment’s data-driven world, securely accessing, visualizing, and analyzing information is crucial for making knowledgeable enterprise selections. Tens of 1000’s of shoppers use Amazon Redshift for contemporary information analytics at scale, delivering as much as 3 times higher price-performance and 7 occasions higher throughput than different cloud information warehouses.

The Amazon Redshift Knowledge API simplifies entry to your Amazon Redshift information warehouse by eradicating the necessity to handle database drivers, connections, community configurations, information buffering, and extra.

With the newly launched characteristic of Amazon Redshift Knowledge API help for single sign-on and trusted identification propagation, you may construct information visualization functions that combine single sign-on (SSO) and role-based entry management (RBAC), simplifying consumer administration whereas implementing applicable entry to delicate info.

As an example, a worldwide sports activities gear firm promoting merchandise throughout a number of areas wants to visualise its gross sales information, which incorporates country-level particulars. To keep up the best stage of entry, the corporate needs to limit information visibility primarily based on the consumer’s function and area. Regional gross sales managers ought to solely see gross sales information for his or her particular area, reminiscent of North America or Europe. Conversely, the worldwide gross sales executives require full entry to your complete dataset, masking all nations.

On this submit, we dive into the newly launched characteristic of Amazon Redshift Knowledge API help for SSO, Amazon Redshift RBAC for row-level safety (RLS) and column-level safety (CLS), and trusted identification propagation with AWS IAM Identification Heart to let company identities hook up with AWS companies securely. We show methods to combine these companies to create an information visualization utility utilizing Streamlit, offering safe, role-based entry that simplifies consumer administration whereas ensuring that your group could make data-driven selections with enhanced safety and ease.

Resolution overview

We use a number of AWS companies and open supply instruments to construct a easy information visualization utility with SSO to entry information in Amazon Redshift with RBAC. The important thing elements that energy the answer are as follows:

IAM Identification Heart and trusted identification propagation – IAM Identification Heart can simplify consumer administration by enabling SSO throughout AWS companies. This enables customers to authenticate with their company credentials managed of their company identification supplier (IdP) like Okta, offering seamless entry to the appliance. We discover how trusted identification propagation permits managing application-level entry management at scale and exercise logging throughout AWS companies, like Amazon Redshift, by propagating and sustaining identification context all through the workflow.
Exterior IdP – We use Okta as an exterior IdP to handle consumer authentication. Okta connects to IAM Identification Heart, permitting customers to authenticate from exterior programs whereas sustaining centralized identification administration inside AWS. This makes positive that consumer entry and roles are persistently maintained throughout each AWS companies and exterior instruments.
Amazon Redshift Serverless workgroup, Amazon Redshift Knowledge API, and Amazon Redshift RBAC – Amazon Redshift is a completely managed information warehouse service that permits for quick querying and evaluation of huge datasets. On this resolution, we use the Redshift Knowledge API, which affords a easy and safe HTTP-based connection to Amazon Redshift, eliminating the necessity for JDBC or ODBC driver-based connections. The Redshift Knowledge API is the beneficial methodology to attach with Amazon Redshift for net functions. We additionally use RBAC in Amazon Redshift to show entry restrictions on gross sales information primarily based on the area column, ensuring that regional gross sales managers solely see information for his or her assigned areas, whereas world gross sales managers have full entry.
Streamlit utility – Streamlit is a broadly used open supply device that permits the creation of interactive information functions with minimal code. On this resolution, we use Streamlit to construct a user-friendly interface the place gross sales managers can view and analyze gross sales information in a visible, accessible format. The applying will combine with Amazon Redshift, offering customers with entry to the info primarily based on their roles and permissions.

The next diagram illustrates the answer structure for SSO with the Redshift Knowledge API utilizing IAM Identification Heart.

The consumer workflow for the info visualization utility consists of the next steps:

The consumer (whether or not a regional gross sales supervisor or world gross sales supervisor) accesses the Streamlit utility, which is built-in with SSO to offer a seamless authentication expertise.
The applying redirects the consumer to authenticate by means of Okta, the exterior IdP. Okta verifies the consumer’s credentials and returns an ID token to the appliance.
The applying makes use of the token issued by Okta to imagine a job and momentary AWS Identification and Entry Administration (IAM) session credentials to name the IAM Identification Heart AssumeRoleWithWebIdentity API and IAM AssumeRole API in later steps.
The applying exchanges the Okta ID token for a token issued by IAM Identification Heart by calling the IAM Identification Heart CreateTokenWithIAM API utilizing the momentary IAM credentials from the earlier step. This token makes positive that the consumer is authenticated with AWS companies and is tied to the IAM Identification Heart consumer profile.
The applying requests an identity-enhanced IAM function session utilizing the IAM Identification Heart token by calling the AssumeRole
The applying makes use of the identity-enhanced IAM function session credentials to securely question Amazon Redshift for gross sales information. The credentials make it possible for solely licensed customers can work together with the Redshift information.
Because the question is processed, Amazon Redshift checks the identification context offered by IAM Identification Heart. It verifies the consumer’s function and group membership, reminiscent of being part of the North American area or the worldwide gross sales supervisor group.
Based mostly on the consumer’s identification and group membership, and utilizing Amazon Redshift RBAC and row-level safety, Amazon Redshift makes an authorization choice. The teams for the illustration may be broadly categorised into the next classes:
1. Regional gross sales managers will probably be granted entry to view gross sales information just for the particular nation or area they handle. As an example, the AMER North American Gross sales Supervisor will solely see gross sales information associated to North America. Equally, the entry management primarily based on EMEA and APAC areas will present row-level safety for the respective areas.
2. The worldwide gross sales managers will probably be granted full entry to all areas, enabling them to view your complete world dataset.

The setup consists of two primary steps:

Provision the assets for IAM Identification Heart, Amazon Redshift and Okta:
1. Allow IAM Identification Heart and configure Okta because the IdP to handle consumer authentication and group provisioning.
2. Create an Okta utility to authenticate customers accessing the Streamlit utility.
3. Arrange an Amazon Redshift IAM Identification Heart connection utility to allow trusted identification propagation for safe authentication.
4. Provision an Amazon Redshift Serverless
5. Create the tables and configure RBAC inside the Redshift workgroup to implement row-level safety for various IAM Identification Heart federated roles, mapped to IAM Identification Heart teams.
Obtain, configure, and run the Streamlit utility:
1. Create a buyer managed utility in IAM Identification Heart for the Redshift Knowledge API shopper (Streamlit utility) to allow safe API-based queries and create the required IAM roles
2. Configure the Streamlit utility.
3. Run the Streamlit utility.

Conditions

You must have the next conditions:

Provision the assets for IAM Identification Heart, Amazon Redshift, and Okta

On this part, we stroll by means of the steps to provision the assets for IAM Identification Heart, Amazon Redshift, and Okta.

Allow IAM Identification Heart and configure Okta because the IdP

Full the next steps to allow IAM Identification Heart and configure Okta because the IdP to handle consumer authentication and group provisioning:

Create the next customers and teams in Okta:
1. Ethan World with e-mail [email protected], in group exec-global
2. Frank Amer with e-mail [email protected], in group amer-sales
3. Alex Emea with e-mail [email protected], in group emea-sales
4. Ming Apac with e-mail [email protected], in group apac-sales

Create an IAM Identification Heart occasion within the AWS Area the place Amazon Redshift goes to be deployed. A company occasion sort is beneficial.
Configure Okta because the identification supply and allow automated consumer and group provisioning. The customers and teams will probably be pushed to IAM Identification Heart utilizing SCIM protocol.

The next screenshot reveals the customers synced in IAM Identification Heart utilizing SCIM protocol.

Create an Okta utility

Full the next steps to create an Okta utility to authenticate customers accessing the Streamlit utility:

Create an OIDC utility in Okta.
1. Copy and save the shopper ID and shopper secret wanted later for the Streamlit utility and the IAM Identification Heart utility to attach utilizing the Redshift Knowledge API.
2. Generate the shopper secret and set sign-in redirect URL and sign-out URL to http://localhost:8501 (we are going to host the Streamlit utility regionally on port 8501).
3. Underneath Assignments, Managed entry, grant entry to everybody.
Create an OIDC IdP on IAM the console. The next screenshot reveals an IdP created on the IAM console.

Arrange an Amazon Redshift IAM Identification Heart connection utility

Full the next steps to create an Amazon Redshift IAM Identification Heart connection utility to allow trusted identification propagation for safe authentication:

On the Amazon Redshift console, select IAM Identification Heart connection within the navigation pane.
Select Create utility.
Title the appliance redshift-data-api-okta-app.
Be aware down the IdP namespace. The default worth AWSIDC is used for this submit.

Within the IAM function for IAM Identification Heart entry part, it’s good to present an IAM function. You may go to the IAM console and create an IAM function known as RedshiftOktaRole with the next coverage and belief relationship. RedshiftOktaRole is utilized by the Amazon Redshift IAM Identification Heart connection utility to handle and work together with IAM Identification Heart.

The coverage connected to the function wants the next permissions:

{
  "Model": "2012-10-17",
  "Assertion": [
    {
      "Effect": "Allow",
      "Action": [
        "sso:DescribeApplication",
        "sso:DescribeInstance"
      ],
      "Useful resource": [
        "arn:aws:sso:::instance/<IAM Identity Center Instance ID>",
        "arn:aws:sso::<AWS Account ID>:application/<IAM Identity Center Instance ID>/*"
      ]
    }
  ]
}

The function makes use of the next belief relationship:

{
  "Model": "2012-10-17",
  "Assertion": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": [
          "redshift.amazonaws.com",
          "redshift-serverless.amazonaws.com"
        ]
      },
      "Motion": [
        "sts:AssumeRole",
        "sts:SetContext"
      ]
    }
  ]
}

Depart Trusted Identification propagation part unchanged, then select Subsequent. You’ve gotten the choice to decide on AWS Lake Formation or Amazon S3 Entry Grants to be used instances like utilizing Amazon Redshift Spectrum to question exterior tables in Lake Formation. In our use case, we solely use Amazon Redshift native tables so we don’t select both.
Within the Configure shopper connections that use third-party IdPs part, select No.
Evaluation and select Create utility.
When the appliance is created, navigate to your IAM Identification Heart connection redshift-data-api-okta-app and select Assign so as to add the teams that have been synced in IAM Identification Heart utilizing SCIM protocol from Okta.

We are going to allow trusted identification propagation and third-party IdP (Okta) on the shopper managed utility for the Redshift Knowledge API in a later step as a substitute of configuring it within the Amazon Redshift connection utility.

The next screenshot reveals the IAM Identification Heart connection utility created on the Amazon Redshift console.

The next screenshot reveals teams assigned to the Amazon Redshift IAM Identification Heart connection for the managed utility.

Provision a Redshift Serverless workgroup

Full the next steps to create a Redshift Serverless workgroup. For extra particulars, confer with Making a workgroup with a namespace.

On the Amazon Redshift console, navigate to the Redshift Serverless dashboard.
Select Create workgroup.
Enter a reputation in your workgroup (for instance, redshift-tip-enabled).
Change the Base capability to eight RPU within the Efficiency and price management
You may configure community and safety primarily based in your digital non-public cloud (VPC) and subnet you need to create the workgroup.
Within the Namespace part, create a brand new namespace in your workgroup. (For instance, redshift-tip-enabled-namespace).
Within the Database title and password part, choose Customise admin consumer credentials and set the admin consumer title and create a password. Be aware them down to make use of in a later step to configure RBAC in Amazon Redshift.
Within the Identification Heart connections part, select Allow for the cluster choice and choose the Amazon Redshift IAM Identification Heart utility created within the earlier step (redshift-data-api-okta-app).
Affiliate an IAM function with the workgroup that has the next insurance policies connected. Make it the default function to make use of.
Depart different settings as default and select Subsequent.
Evaluation the settings and create the workgroup.

Wait till the workgroup is obtainable earlier than persevering with to the subsequent steps.

Create the tables and configure RBAC inside the Redshift Serverless workgroup

Subsequent, you employ the Amazon Redshift Question Editor V2 on the Amazon Redshift console to connect with the workgroup you simply created. You create the tables and configure the Amazon Redshift roles akin to Okta teams for the teams in IAM Identification Heart and use the RBAC coverage to grant customers privileges to view information just for their areas. Full the next steps:

On the Amazon Redshift console, open the Question Editor V2.
Select the choices menu (three dots) subsequent to the Redshift workgroup title and select Edit connection.
Choose Different methods to attach and use the database consumer title and password to attach.

Within the question editor, run the next code to create the gross sales desk and cargo the info from Amazon Easy Storage Service (Amazon S3):

# Create the desk
CREATE TABLE IF NOT EXISTS public.sales_data (
    SKU VARCHAR(50),
    Product_Name VARCHAR(255),
    Class VARCHAR(100),
    Amount INT,
    Sales_Price DECIMAL(10,2),
    Timestamp TIMESTAMP,
    Metropolis VARCHAR(100),
    Region_Code VARCHAR(10),
    Nation VARCHAR(10),
    Latitude DECIMAL(10,6),
    Longitude DECIMAL(10,6),
    Inhabitants INT,
    Elevation INT,
    Timezone VARCHAR(50)
);

# Load information from S3 to desk
COPY public.sales_data
FROM 's3://redshift-blogs/redshift-data-api-idc/sales_data.csv'
IAM_ROLE default
CSV
IGNOREHEADER 1
DELIMITER ','
TIMEFORMAT 'auto';

# Create Redshift roles for the teams in IDC, the function format is Namespace:IDCGroupName
CREATE ROLE "AWSIDC:amer-sales";
CREATE ROLE "AWSIDC:emea-sales";
CREATE ROLE "AWSIDC:apac-sales";
CREATE ROLE "AWSIDC:exec-global";

--Create RLS coverage
CREATE RLS POLICY eu_region_filter
WITH (timezone VARCHAR(50))
USING (timezone LIKE 'Europe%');

CREATE RLS POLICY apac_region_filter
WITH (timezone VARCHAR(50))
USING (timezone LIKE 'Asia%');

CREATE RLS POLICY amer_region_filter
WITH (timezone VARCHAR(50))
USING (timezone LIKE 'America%');

--Connect coverage
ATTACH RLS POLICY eu_region_filter ON sales_data TO ROLE "AWSIDC:emea-sales";
ATTACH RLS POLICY apac_region_filter ON sales_data TO ROLE "AWSIDC:apac-sales";
ATTACH RLS POLICY amer_region_filter ON sales_data TO ROLE "AWSIDC:amer-sales";

--Activate RLS on desk
ALTER TABLE public.sales_data ROW LEVEL SECURITY ON;
GRANT IGNORE RLS TO ROLE "AWSIDC:exec-global";

IAM Identification Heart will map the teams into the Redshift roles within the format of Namespace:IDCGroupName. Due to this fact, create the function title as AWSIDC:emea-sales and so forth to match them with Okta group names synced in IAM Identification Heart. The customers will probably be created robotically inside the teams as they log in utilizing SSO into Amazon Redshift.

Obtain, configure, and run the Streamlit utility

On this part, we stroll by means of the steps to obtain, configure, and run the Streamlit utility.

Create a buyer managed utility in IAM Identification Heart for the Redshift Knowledge API shopper

With a view to begin a trusted identification propagation workflow and permit Amazon Redshift to make authorization selections primarily based on the customers and teams from IAM Identification Heart (provisioned from the exterior IdP), you want an identity-enhanced IAM function session.

This requires a few IAM roles and a buyer managed utility in IAM Identification Heart to deal with the belief relationship between the exterior IdP and IAM Identification Heart and management entry for the Redshift Knowledge API shopper, on this case, the Streamlit utility.

First, you create two IAM roles, then you definately create a buyer managed utility for the Streamlit utility. Full the next steps:

Create a short lived IAM function (we named it IDCBridgeRole) to alternate the token with IAM Identification Heart (assuming you don’t have an present IAM identification to make use of). This function will probably be assumed by the Streamlit utility with AssumeRoleWithWebIdentity to get a short lived set of function credentials to name the CreateTokenWithIAM and AssumeRole APIs to get the identity-enhanced function session.

Connect the next coverage the function:

{
    "Model": "2012-10-17",
    "Assertion": [
        {
            "Effect": "Allow",
            "Action": "sso-oauth:CreateTokenWithIAM",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "sts:SetContext",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "sts:AssumeRole",
            "Resource": "*"
        }
    ]
}

Within the belief relationship, present your AWS account ID and IdP’s URL. The trusted principal to make use of is the Amazon Useful resource Title (ARN) of oidc-provider you created earlier.

{
    "Model": "2012-10-17",
    "Assertion": [
        {
            "Effect": "Allow",
            "Principal": {
                "Federated": "arn:aws:iam::<accountid>:oidc-provider/<your-idp-domain>"
            },
            "Action": "sts:AssumeRoleWithWebIdentity"
        }
    ]
}

Create an IAM function with permissions to entry the Redshift Knowledge API (we named it RedshiftDataAPIClientRole). This function will probably be assumed by the Streamlit utility with the improved identities from IAM Identification Heart after which used to authenticate requests to the Redshift Knowledge API.
1. Connect the AmazonRedshiftDataFullAccess AWS managed coverage. AWS recommends utilizing the precept of least privilege in your IAM coverage.
2. Prohibit the belief relationship to the IDCBridgeRole ARN created within the earlier step), and supply your AWS account ID:
```
{
    "Model": "2012-10-17",
    "Assertion": [
        {
            "Sid": "Statement1",
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::<accountid>:role/IDCBridgeRole"
            },
            "Action": [
                "sts:AssumeRole",
                "sts:SetContext"
            ]
        }
    ]
}
```

Now you may create the shopper managed utility.

On the IAM Identification Heart console, select Functions within the navigation pane.
Select Add utility.
Select I’ve an utility I need to setup, choose the OAuth 2.0 utility sort, and select Subsequent.
Enter a reputation for the appliance, for instance, RedshiftStreamlitDemo.
In Person and group task methodology, select Don’t require task. This implies all of the customers provisioned in IAM Identification Heart from Okta can use their Okta credentials to register to the Streamlit utility. You may alternatively choose the Require assignments choice and decide the customers and teams you need to permit entry to the appliance.
Within the AWS entry portal part, select Not seen, then select Subsequent.
Within the Authentication with trusted token issuer part, choose Create trusted token issuer, then enter the Okta issuer URL and enter a reputation for the trusted token issuer.
Within the map attribute, use the default e-mail to e-mail mapping between the exterior IdP attribute and IAM Identification Heart attribute, then create the trusted token issuer.
Choose the trusted token issuer you simply created.
Within the Aud declare part, use the shopper ID of the Okta utility you famous earlier, then select Subsequent.

Within the Specify utility credentials part, select Edit the appliance coverage and use the next coverage:

{
  "Model": "2012-10-17",
  "Assertion": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "redshift-data.amazonaws.com"
      },
      "Action": "sso-oauth:*",
      "Resource": "*"
    }
  ]
}

Select Submit.

After the appliance is created, you may view it in on the IAM Identification Heart.

Select Functions within the navigation pane, and find the Buyer managed functions tab.

Select the appliance to navigate to the appliance particulars web page.
Within the Trusted functions for identification propagation part, select Specify trusted functions and choose the setup sort as Particular person functions and specify entry, then select Subsequent.
Select Amazon Redshift because the service, then select Subsequent.
Within the Software that may obtain requests part, select the Amazon Redshift IAM Identification Heart utility you created, then select Subsequent.
Within the Entry Scopes to use part, verify the redshift:join
Evaluation after which select Belief utility.

Configure and run the Streamlit utility

Now that you’ve the roles and the shopper managed utility in IAM Identification Heart, you may create an identity-enhanced IAM function session, which is essentially the most important step to allow trusted identification propagation. Following steps present an outline of Streamlit utility code to create the identity-enhanced IAM function session.

Authenticate with and retrieve the id_token from the exterior IdP (Okta).
Name CreateTokenWithIAM utilizing the exterior IdP issued id_token to acquire an IAM Identification Heart issued id_token.
Use AssumeRoleWithWebIdentity to acquire momentary IAM credentials (by assuming IDCBridgeRole, defined later).
Extract the sts:identity_context from the IAM Identification Heart issued id_token.
Assume the function RedshiftDataAPIClientRole with the AssumeRole API and insert the sts:identity_context to acquire the identity-enhanced IAM function session credentials.

Now you should utilize these credentials to make requests to the Redshift Knowledge API, and Amazon Redshift will be capable of use the identification context for authorization selections.

At this level, it’s best to have all of the required assets for creating the Streamlit utility. Full the next steps to check the Streamlit utility:

Obtain the Streamlit utility code and modify the configuration part of the code primarily based on the assets provisioned earlier:

# TIP Token alternate configuration
AWS_REGION = "<YOUR AWS REGION>" # us-east-1
TOKEN_EXCHANGE_APP_ARN = "<YOUR IDC CUSTOM APP ARN>" # The ARN of the IDC customer-managed-App created earlier
TOKEN_GRANT_TYPE = "urn:ietf:params:oauth:grant-type:jwt-bearer" # mounted worth, please do not change
TEMP_ROLE_ARN = "<TEMP ROLE ARN>" # The function created on this step for customers to imagine with AssumeRoleWithWebIdentity(IDCBridgeRole)
ENHANCED_ROLE_ARN = "<ENHANCED ROLE ARN>" # The function created on this step for customers to imagine for the Identification-enhanced function session with IAM Identification Heart(RedshiftDataAPIClientRole)
IDENHANCED_ROLE_SESSION_NAME = "rs-idc-tip-session" # Use any title for the session 
ROLE_DURATION_SECS = 3600  # 1 hour

# Okta OAuth configuration, change with your individual Okta Area
OKTA_DOMAIN = "<YOUR OKTA DOMAIN>"
AUTHORIZE_URL = f"https://{OKTA_DOMAIN}/oauth2/v1/authorize"
TOKEN_URL = f"https://{OKTA_DOMAIN}/oauth2/v1/token"
REFRESH_TOKEN_URL = f"https://{OKTA_DOMAIN}/oauth2/v1/token"
REVOKE_TOKEN_URL = f"https://{OKTA_DOMAIN}/oauth2/v1/revoke"
LOGOUT_URL = f"https://{OKTA_DOMAIN}/oauth2/v1/logout"
CLIENT_ID = "<OKTA CLIENT ID>" # The shopper id of the Okta app created for the Streamlit app in 2.
CLIENT_SECRET = "<OKTA CLIENT SECRET>" # The shopper id of the Okta app created for the Streamlit app in 2.
REDIRECT_URI = "http://localhost:8501" # That is for dev/take a look at function solely
SCOPE = "openid profile e-mail" # Please don't change
WORKGROUP_NAME = "<your-redshift-workgroup-we-used:redshift-tip-enabled>" #The title of the created Redshift Workgroup
DATABASE = "dev" # The database set for the Workgroup

We advocate internet hosting this utility on an Amazon Elastic Compute Cloud (Amazon EC2) occasion for manufacturing use instances, and utilizing AWS Secrets and techniques Supervisor for delicate info just like the CLIENT_ID and CLIENT_SECRET offered as configuration parameters within the code for simplicity.

For this instance, we use the Okta group URL (/oauth2/v1/). You need to use the shopper authorization servers as properly, for instance, the default authorization server, however be sure that all URLs are utilizing the identical authorization server. Confer with Authorization servers for extra details about authorization servers in Okta.

After you modify the script for the Streamlit utility, you may run it utilizing a Python digital atmosphere.

Create a Python digital atmosphere. The applying has been examined efficiently with variations v3.12.8 and v3.12.2.

It’s essential to set up the next packages, that are required libraries for the Streamlit utility code you downloaded in your digital atmosphere:

streamlit
streamlit_oauth
boto3
pyjwt
pydeck
pandas

You may set up these libraries immediately utilizing the next command with the necessities file:

pip set up -r https://redshift-blogs.s3.us-east-1.amazonaws.com/redshift-data-api-idc/necessities.txt

Check the Streamlit utility within the Python digital atmosphere with the next command:
```
streamlit run /path/to/st_app.py
```
Log in with the consumer [email protected] from the apac-sales group.

The identity-enhanced function session credentials will show on the highest of the web page after profitable authentication with Okta.

For the APAC area supervisor, it’s best to solely see the info from the nations within the Asia-Pacific area primarily based on the row-level safety filter you configured earlier.

Log off and log again in with the worldwide govt consumer, [email protected] from the exec-global

You must see the info in all areas.

You may strive different regional customers’ logins and it’s best to see solely the info within the area they belong to.

Trusted identification propagation deep dive

On this part, you stroll by means of the Python code of the Streamlit utility and clarify how trusted identification propagation works. The next is an evidence of key elements of the appliance code.

primary()

The primary() perform of the Streamlit utility implements the previous steps to get the identity-enhanced IAM function session utilizing the get_id_enhanded_session() perform, which wraps the login to get the identity-enhanced function session credentials:

def primary():
    # Create OAuth2Component occasion
    oauth2 = OAuth2Component(
        CLIENT_ID, 
        CLIENT_SECRET, 
        AUTHORIZE_URL, 
        TOKEN_URL, 
        REFRESH_TOKEN_URL, 
        REVOKE_TOKEN_URL)
    
    # Different setup code omitted
    
    # Deal with OAuth authentication with Okta
    if not st.session_state.is_authenticated or is_token_expired():
        # Present the login button if not authenticated
        st.title("Login to the Demo app")
        consequence = oauth2.authorize_button("Login with Okta", REDIRECT_URI, SCOPE)
        if consequence and "token" in consequence:
            # Save the token in session state and mark the consumer as authenticated
            st.session_state.token = consequence.get("token")
            st.session_state.user_email = get_user_email_from_token(st.session_state.token.get("id_token"))
            st.session_state.aws_creds = get_id_enhanced_session(st.session_state.token.get("id_token"))
            st.session_state.is_authenticated = True
            st.rerun()
    else:
        
        st.json(st.session_state.aws_creds)
        st.title("Whole Gross sales by Metropolis")
    
        if not is_token_expired():
            # Use the improved credentials to create the Redshift shopper
            redshift_client = boto3.shopper("redshift-data", region_name=AWS_REGION,
                                        aws_access_key_id=st.session_state.aws_creds['AccessKeyId'],
                                        aws_secret_access_key=st.session_state.aws_creds['SecretAccessKey'],
                                        aws_session_token=st.session_state.aws_creds['SessionToken'])
        else:
            st.error("Session expired. Please re-authenticate.")
            logout()
            
    # extra code for question execution and information visualizetion omitted

We use the Streamlit st.session_state offered by Streamlit to retailer necessary session states, together with the authentication standing in addition to extra info like consumer info and the AWS identity-enhanced function session credentials.

get_id_enhanced_session()

The get_id_enhanced_session() perform code has three steps:

We use the id_token (variable title: jwt_token) from Okta in JWT format to name the AssumeRoleWithWebIdentity API to imagine the function IDCBridgeRole. It is because the consumer doesn’t have any AWS credentials to work together with the IAM Identification Heart API. Should you plan to host this utility in an AWS atmosphere with an IAM function obtainable, for instance, on an EC2 occasion, you should utilize the function related to Amazon EC2 to make the decision to the IAM Identification Heart APIs with out creating IDCBridgeRole, however be sure that the EC2 function has the required permissions we specified for IDCBridgeRole.
After we’ve the credentials of the momentary function, we use them to make a name to the CreateTokenWithIAM API of IAM Identification Heart. This API handles the alternate of tokens by taking within the id_token from Okta and returning an IAM Identification Heart issued token, which will probably be used later to get the identity-enhanced function session. For extra info, confer with the CreateTokenWithIAM API reference.
Lastly, we extract the sts:identity_context from the IAM Identification Heart issued id_token and cross it to the AWS Safety Token Service (AWS STS) AssumeRole That is finished by together with the sts:identity_context within the ContextAssertion parameter inside ProvidedContexts, together with ProviderArn set to arn:aws:iam::aws:contextProvider/IdentityCenter.

def get_id_enhanced_session(jwt_token):
    """
    Obtains an identity-enhanced session by assuming a short lived IAM function,
    making a token with IAM, and assuming an enhanced function session.
    
    Args:
        jwt_token (str): The JWT id token from the identification supplier.
    
    Returns:
        dict or None: The improved session credentials if profitable, in any other case None.
    """
    logging.information("Beginning identity-enhanced session course of.")

    # Step 1: Assume a short lived IAM function with the offered JWT token
    temp_credentials = assume_role_with_web_identity(jwt_token)
    if not temp_credentials:
        logging.error("Did not assume function with net identification.")
        return None

    # Step 2: Use the momentary credentials to create a token with IAM
    id_token = create_token_with_iam(jwt_token, temp_credentials)
    if not id_token:
        logging.error("Did not create ID token with IAM.")
        return None

    # Step 3: Use the ID token to imagine an enhanced function session
    enhanced_creds = assume_enhanced_role_session(id_token, temp_credentials)
    if not enhanced_creds:
        logging.error("Did not assume enhanced function session.")
        return None

    logging.information("Efficiently obtained identity-enhanced session credentials.")
    return enhanced_creds

assume_role_with_web_identity()

The assume_role_with_web_identity() perform code is as follows. We initialize the STS shopper, decode the JWT token, after which assume the function with the net identification.

def assume_role_with_web_identity(jwt_token):
    """
    Assumes an IAM function utilizing an internet identification token and returns the momentary credentials.

    Args:
        jwt_token (str): The JWT token for authentication, usually issued by an exterior identification supplier.

    Returns:
        dict: Short-term IAM credentials (Entry Key, Secret Key, Session Token) or None if an error happens.
    """
    strive:
        # Initialize the STS shopper
        sts_client = boto3.shopper('sts', region_name=AWS_REGION)
        
        # Decode the JWT token with out verifying signature (for debugging functions)
        decoded_jwt = jwt.decode(jwt_token, choices={"verify_signature": False})
        logging.debug(f"Decoded JWT Token: {decoded_jwt}")

        # Put together the request for AssumeRoleWithWebIdentity
        assume_role_request = {
            'RoleArn': TEMP_ROLE_ARN,
            'RoleSessionName': 'WebIdentitySession',
            'WebIdentityToken': jwt_token,
            'DurationSeconds': DURATION_SECS  # 1 hour
        }

        # Name the AssumeRoleWithWebIdentity API
        assume_role_response = sts_client.assume_role_with_web_identity(**assume_role_request)
        
        # Extract the momentary credentials from the response
        temp_credentials = assume_role_response['Credentials']
        logging.information("Short-term credentials efficiently obtained.")
        
        # Return the momentary credentials
        return temp_credentials

    besides ClientError as e:
        logging.error(f"Error calling AssumeRoleWithWebIdentity: {e}")
        return None
    besides jwt.ExpiredSignatureError:
        logging.error("JWT token has expired.")
        return None
    besides jwt.DecodeError:
        logging.error("Error decoding JWT token.")
        return None
    besides Exception as e:
        logging.error(f"Sudden error: {e}")
        return None

create_token_with_iam()

The create_token_with_iam() perform code is known as to get the id_token from IAM Identification Heart. The jwt_token is the id_token in JWT format issued by Okta; the id_token is the IAM Identification Heart issued id_token.

def create_token_with_iam(jwt_token, temp_credentials):
    """
    Creates an IAM token utilizing the offered JWT token and momentary credentials.

    Args:
        jwt_token (str): The JWT token to alternate for an IAM token.
        temp_credentials (dict): Short-term AWS credentials for assuming the function.
    
    Returns:
        str or None: The IAM token if profitable, in any other case None.
    """
    logging.information("Beginning token creation course of with IAM.")
    
    # Initialize the SSO OIDC shopper with momentary credentials
    strive:
        sso_oidc_client = boto3.shopper(
            'sso-oidc', 
            region_name=AWS_REGION, 
            aws_access_key_id=temp_credentials['AccessKeyId'],
            aws_secret_access_key=temp_credentials['SecretAccessKey'],
            aws_session_token=temp_credentials['SessionToken']
        )
    besides Exception as e:
        logging.error(f"Error initializing SSO OIDC shopper: {e}")
        return None

    # Put together the request for CreateTokenWithIAM
    token_request = {
        'clientId': TOKEN_EXCHANGE_APP_ARN,
        'grantType': TOKEN_GRANT_TYPE,
        'assertion': jwt_token
    }

    # Name the CreateTokenWithIAM API
    strive:
        token_result = sso_oidc_client.create_token_with_iam(**token_request)
        id_token = token_result['idToken']
        logging.information(f"Efficiently obtained ID Token: {id_token}")
        return id_token
    besides ClientError as e:
        logging.error(f"Error calling CreateTokenWithIAM API: {e}")
        return None
    besides KeyError as e:
        logging.error(f"Lacking anticipated discipline in response: {e}")
        return None

Within the CreateTokenWithIAM name, we cross the next parameters:

clientId – The ARN of the IAM Identification Heart utility for the Redshift Knowledge API shopper
grantType – urn:ietf:params:oauth:grant-type:jwt-bearer
assertion – The id_token (jwt_token) issued by Okta

The idToken issued by IAM Identification Heart is returned.

assume_enhanced_role_session()

The assume_enhanced_role_session() perform makes use of the ID token to imagine an identity-enhanced function session:

def assume_enhanced_role_session(id_token, temp_credentials):
    """
    Assumes an identity-enhanced IAM function session utilizing the offered ID token and momentary credentials.

    Args:
        id_token (str): The ID token containing the identification context.
        temp_credentials (dict): Short-term AWS credentials for assuming the function.

    Returns:
        dict or None: The credentials for the identity-enhanced IAM function session, or None on failure.
    """
    logging.information("Extracting identification context from ID token.")
    identity_context = extract_identity_context_from_id_token(id_token)

    if not identity_context:
        logging.error("Did not extract identification context from ID token.")
        return None

    strive:
        # Initialize STS shopper with momentary credentials
        sts_client = boto3.shopper(
            'sts',
            region_name=AWS_REGION,
            aws_access_key_id=temp_credentials['AccessKeyId'],
            aws_secret_access_key=temp_credentials['SecretAccessKey'],
            aws_session_token=temp_credentials['SessionToken']
        )

        # Put together AssumeRole request with identification context
        assume_role_request = {
            'RoleArn': ENHANCED_ROLE_ARN,
            'RoleSessionName': IDENHANCED_ROLE_SESSION_NAME,
            'DurationSeconds': ROLE_DURATION_SECS,
            'ProvidedContexts': [{
                'ContextAssertion': identity_context,
                'ProviderArn': "arn:aws:iam::aws:contextProvider/IdentityCenter"
            }]
        }

        # Name the AssumeRole API
        logging.information("Calling STS AssumeRole for identity-enhanced session.")
        assume_role_response = sts_client.assume_role(**assume_role_request)

        enhanced_role_credentials = assume_role_response['Credentials']
        logging.information("Efficiently assumed enhanced function.")
        
        return enhanced_role_credentials

    besides ClientError as e:
        logging.error(f"Error calling AssumeRole: {e}")
        return None

extract_identity_context_from_id_token()

The extract_identity_context_from_id_token() perform extracts the sts:identity_context:

def extract_identity_context_from_id_token(id_token):
    """
    Extracts the identification context from a decoded JWT token.

    Args:
        id_token (str): The JWT token containing identification context.

    Returns:
        dict or None: The extracted identification context if obtainable, in any other case None.
    """
    logging.information("Decoding ID token to extract identification context.")

    strive:
        # Decode the JWT token (with out signature verification)
        decoded_jwt = jwt.decode(id_token, choices={"verify_signature": False})

        logging.debug(f"Decoded JWT Claims: {decoded_jwt}")

        # Extract the identification context from the token
        for key in ('sts:identity_context', 'sts:audit_context'):
            if key in decoded_jwt:
                return decoded_jwt[key]

        logging.warning("No legitimate identification context discovered within the token.")
        return None

    besides Exception as e:
        logging.error(f"Error decoding JWT: {e}")
        return None

Now you’ve got the identity-enhanced function session credentials to name the Amazon Redshift Knowledge API.

execute_statement() and fetch_results()

The execute_statement() and fetch_results() features show methods to run Redshift queries and retrieve question outcomes with trusted identification propagation for visualization:

def execute_statement(sql, redshift_client):
    """
    Executes a SQL assertion on Amazon Redshift utilizing the offered Redshift Knowledge API shopper.

    Args:
        sql (str): The SQL question to execute.
        redshift_client (boto3.shopper): The Redshift Knowledge API shopper.

    Returns:
        str: The execution ID of the assertion.

    Raises:
        ClientError: If an error happens throughout execution.
    """
    strive:
        response = redshift_client.execute_statement(
            WorkgroupName=WORKGROUP_NAME,
            Database=DATABASE,
            Sql=sql 
        )
        return response["Id"]
    
    besides ClientError as e:
        error_code = e.response.get('Error', {}).get('Code', '')
        
        if error_code == 'ExpiredTokenException':
            logging.error("Session expired. Logging out...")
            logout()
        else:
            logging.error(f"Error executing assertion: {e}")
            elevate
            
def fetch_results(statement_id, redshift_client):
    """
    Fetches question outcomes from the Redshift Knowledge API.

    Args:
        statement_id (str): The execution ID of the assertion.
        redshift_client (boto3.shopper): The Redshift Knowledge API shopper.

    Returns:
        record: An inventory of data from the question consequence.
    """
    strive:
        response = redshift_client.get_statement_result(Id=statement_id)
        return response.get("Information", [])
    
    besides ClientError as e:
        logging.error(f"Error fetching question outcomes: {e}")
        elevate

Conclusion

On this submit, we confirmed methods to create a third-party utility backed by analytics insights arriving from Amazon Redshift securely utilizing OIDC. With Redshift Knowledge API help of IAM Identification Heart integration, you may hook up with Amazon Redshift utilizing SSO from the IdP of your alternative. You may lengthen this methodology to authenticate different AWS companies that help trusted identification propagation, reminiscent of Amazon Athena and Amazon QuickSight, enabling fine-grained entry management for IAM Identification Heart customers and teams throughout your AWS ecosystem. We encourage you to arrange your utility utilizing IAM Identification Heart integration and unify your entry management immediately out of your IdP throughout all IAM Identification Heart supported AWS companies.

For extra info on AWS companies and functions that help trusted identification propagation, confer with Trusted identification propagation overview.

In regards to the Authors

Songzhi Liu is a Principal Large Knowledge Architect with the AWS Identification Options group. On this function, he collaborates intently with AWS clients and cross-functional groups to design and implement scalable information architectures, specializing in integrating massive information and machine studying options to boost identification consciousness inside the AWS ecosystem.

Rohit Vashishtha is a Senior Analytics Specialist Options Architect at AWS primarily based in Dallas, Texas. He has over 19 years of expertise architecting, constructing, main, and sustaining massive information platforms. Rohit helps clients modernize their analytic workloads utilizing the breadth of AWS companies and ensures that clients get one of the best value/efficiency with utmost safety and information governance.

Fei Peng is a Senior Software program Growth Engineer working within the Amazon Redshift group, the place he leads the event of Redshift Knowledge API, enabling seamless and scalable entry to cloud information warehouses.

Yanzhu Ji is a Product Supervisor within the Amazon Redshift group. She has expertise in product imaginative and prescient and technique in industry-leading information merchandise and platforms. She has excellent ability in constructing substantial software program merchandise utilizing net growth, system design, database, and distributed programming strategies. In her private life, Yanzhu likes portray, images, and enjoying tennis.